Copy number alterations detected by whole-exome and whole-genome sequencing of esophageal adenocarcinoma

Abstract

Background: Esophageal adenocarcinoma (EA) is among the leading causes of cancer mortality, especially in developed countries. A high level of somatic copy number alterations (CNAs) accumulates over the decades in the progression from Barrett's esophagus, the precursor lesion, to EA. Accurate identification of somatic CNAs is essential to understand cancer development. Many studies have been conducted for the detection of CNA in EA using microarrays. Next-generation sequencing (NGS) technologies are believed to have advantages in sensitivity and accuracy to detect CNA, yet no NGS-based CNA detection in EA has been reported.

Results: In this study, we analyzed whole-exome (WES) and whole-genome sequencing (WGS) data for detecting CNA from a published large-scale genomic study of EA. Two specific comparisons were conducted. First, the recurrent CNAs based on WGS and WES data from 145 EA samples were compared to those found in five previous microarray-based studies. We found that the majority of the previously identified regions were also detected in this study. Interestingly, some novel amplifications and deletions were discovered using the NGS data. In particular, SKI and PRKCZ detected in a deletion region are involved in transforming growth factor-β pathway, suggesting the potential utility of novel biomarkers for EA. Second, we compared CNAs detected in WGS and WES data from the same 15 EA samples. No large-scale CNA was identified statistically more frequently by WES or WGS, while more focal-scale CNAs were detected by WGS than by WES.

Conclusions: Our results suggest that NGS can replace microarrays to detect CNA in EA. WGS is superior to WES in that it can offer finer resolution for the detection, though if the interest is on recurrent CNAs, WES can be preferable to WGS for its cost-effectiveness.

Fig. 1

Segmented copy number ratio profiles in WES and WGS. The x-axis represents the samples. The y-axis represents the chromosomes. a WES data. b WGS data

Fig. 2

Genomic positions of RCNAs detected in 145 WES data. The x-axis represents the normalized amplification signals (top) and significance by q value (bottom). The green line indicates the significance cutoff at q = 0.25. a Amplification regions. b Deletion regions

Fig. 3

Genomic positions of RCNAs detected in 15 WGS data. The x-axis represents the normalized amplification signals (top) and significance by q value (bottom). The green line indicates the significance cutoff at q = 0.25. a Amplification regions. b Deletion regions